A user wake-up method and system for an IoT smart electric bed

Abstract

The present invention discloses a user awakening method and system for an IoT smart electric bed, the method comprising determining a target awakening method for awakening a user, including monitoring the user's sleep state to obtain the user's sleep cycle stage, and recommending a first awakening method; recommending a second awakening method according to the user's identity information and health status information; generating a third awakening method according to the user's customized awakening setting information; determining a target awakening method according to the first awakening method, the second awakening method, and the third awakening method; performing an awakening operation on the user, monitoring the user's awakening state during the awakening process and generating a curve of the awakening state change, and adaptively adjusting the target awakening method according to the awakening state change curve. The present invention considers multi-dimensional influencing factors to recommend user awakening methods, and adaptively adjusts the awakening method according to the user's awakening state to match the user's situation and actual needs, thereby improving the user's awakening experience.

Description

Technical Field

[0001] The present invention relates to the field of smart home control technology, and in particular to a user wake-up method and system for an Internet of Things smart electric bed. Background Art

[0002] Smart electric beds are an important part of the smart home field. They combine the adjustable functions of traditional electric beds with modern intelligent technology to provide users with a more comfortable and personalized sleeping experience. Regarding the user wake-up process of smart electric beds, although some smart beds have wake-up functions, they cannot provide targeted wake-up strategies because they do not take individual differences into account. In some scenarios, even if users can set it themselves, the wake-up method set by users can only meet the user's preferences. There is a lack of scientific and healthy guidance, which leads to unhealthy wake-up methods and affects the user's physical health. In addition, the current wake-up method considers relatively single factors. During the wake-up process, it often only mechanically executes the preset program and lacks a dynamic adjustment mechanism. Therefore, the degree of wake-up diversity is low and cannot meet the user's demand for a diverse wake-up experience. Summary of the invention

[0003] In order to solve at least one of the technical problems mentioned above, the present invention provides a user wake-up method and system for an IoT smart electric bed.

[0004] In a first aspect, the present invention provides a method for waking up a user of an IoT smart electric bed, the method comprising:

[0005] Determine the target wakeup method used to wake the user, including:

[0006] Monitor the user's sleep state to obtain the user's sleep cycle stage, and recommend a corresponding first wake-up method according to different sleep cycle stages;

[0007] Obtaining the user's identity information and health status information, and recommending a second wake-up method based on the user's identity information and health status information;

[0008] Obtaining user's customized wake-up setting information, and generating a third wake-up method according to the customized wake-up setting information;

[0009] Determine a target awakening method according to the first awakening method, the second awakening method and the third awakening method;

[0010] Perform a wake-up operation on the user according to the target wake-up method, monitor the user's wake-up state during the wake-up process and generate a wake-up state change curve, and adaptively adjust the target wake-up method according to the wake-up state change curve.

[0011] Preferably, determining the target wake-up mode according to the first wake-up mode, the second wake-up mode and the third wake-up mode includes:

[0012] constructing an evaluation factor set according to the first awakening method, the second awakening method, and the third awakening method, and determining a corresponding level set;

[0013] Based on each factor in the evaluation factor set, a corresponding fuzzy evaluation matrix is ​​constructed, and the items in the fuzzy evaluation matrix are used to represent the membership of different evaluation factors at different levels;

[0014] The weight of each factor is determined by using the analytic hierarchy process, and the fuzzy evaluation vector is determined according to the weight of each factor and the fuzzy evaluation matrix;

[0015] The fuzzy evaluation vector is compared with the grade set, the target grade corresponding to the maximum membership degree and the target factor corresponding to the target grade are screened out, and the awakening method under the target factor is used as the target awakening method.

[0016] Preferably, the user's identity information includes the user's age and occupation information; the health status information includes the user's alcohol influence status, physiological cycle status and physical disease status.

[0017] Preferably, the target awakening method includes awakening time, awakening action and awakening auxiliary environment setting; the awakening action includes any one or more of body position change awakening, biological vibration awakening and voice control awakening.

[0018] Preferably, performing a wake-up operation on the user according to the target wake-up mode further includes:

[0019] Monitor the user's body motion data during the awakening process, and determine the user's falling risk under the current target awakening method based on the user's body motion data;

[0020] When it is determined that the falling risk is greater than the preset threshold, an alarm prompt is generated and the target wake-up method is optimized.

[0021] Preferably, determining the falling risk of the user in the current target awakening mode according to the user's body motion data includes:

[0022] Detect the weight distribution of the user on the electric bed surface and obtain the average weight distribution of the user outside the safety range of the bed surface;

[0023] Detect the user's heart rate variability and user activity, and calculate the user's wakefulness coefficient based on the heart rate variability;

[0024] Get the head lift angle of the electric bed, and calculate the fall risk index based on the head lift angle, average weight distribution, user activity, and wakefulness coefficient:

[0025] FRI=w1G×cosθ+w2(1-M)+w3C s +w4(E e +E h );

[0026] Where FRI represents the fall risk index, G represents the average weight distribution of the user outside the safe range of the bed surface, M represents the user's activity, θ represents the angle of the head of the bed, and C s Indicates the user's awake state coefficient, E e 、E h They represent environmental compensation factor and age compensation factor respectively, and w1, w2, w3, and w4 represent weights respectively.

[0027] In a second aspect, the present invention further provides a user wake-up system for an IoT smart electric bed, the system comprising:

[0028] The wake-up mode determination module is used to determine the target wake-up mode for waking up the user, including:

[0029] A first recommendation unit is used to monitor the user's sleep state to obtain the user's sleep cycle stage, and recommend a corresponding first wake-up method according to different sleep cycle stages;

[0030] A second recommendation unit, used to obtain the identity information and health status information of the user, and recommend a second wake-up method according to the identity information and health status information of the user;

[0031] A third recommendation unit, configured to obtain user-defined wake-up setting information and generate a third wake-up method according to the customized wake-up setting information;

[0032] A target mode generating unit, configured to determine a target wake-up mode according to the first wake-up mode, the second wake-up mode and the third wake-up mode;

[0033] The adaptive adjustment module is used to perform a wake-up operation on the user according to the target wake-up method, monitor the user's wake-up state during the wake-up process and generate a wake-up state change curve, and adaptively adjust the target wake-up method according to the wake-up state change curve.

[0034] In a third aspect, the present invention further provides an electronic device comprising: a processor and a memory, wherein the memory is used to store computer program code, wherein the computer program code comprises computer instructions, and when the processor executes the computer instructions, the electronic device executes the method as described in the first aspect above and any possible implementation thereof.

[0035] In a fourth aspect, the present invention further provides a computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, wherein the computer program includes program instructions, and when the program instructions are executed by a processor of an electronic device, the processor executes the method as described in the first aspect above and any possible implementation thereof.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] 1) The user awakening method provided by the present invention can monitor the user's sleep state to obtain the user's sleep cycle stage, recommend the corresponding first awakening method according to different sleep cycle stages; obtain the user's identity information and health status information, and recommend the second awakening method according to the user's identity information and health status information; obtain the user's custom awakening setting information, and generate a third awakening method according to the custom awakening setting information; determine the target awakening method according to the first awakening method, the second awakening method and the third awakening method. The first awakening method is mainly recommended scientifically based on a scientific sleep cycle, and the second awakening method mainly considers the impact of the user's identity information and health status information on user awakening, wherein the user's identity information includes the user's age and occupation information; the health status information includes the user's alcohol influence status, physiological cycle status and physical disease status. The third awakening method is mainly based on the user's own preferences. By combining these three methods, the best target awakening method in the user's current awakening scenario is determined, thereby satisfying the user's preferences and providing a scientific and healthy awakening method, which is more conducive to guiding the user's healthy work and rest.

[0038] 2) When the present invention performs a wake-up operation on the user according to the target wake-up method, the user's wake-up state is monitored during the wake-up process and a change curve of the wake-up state is generated. The target wake-up method is adaptively adjusted according to the change curve of the wake-up state. Compared with the existing mechanized wake-up program, the present invention can dynamically adjust the wake-up method. For example, as the user's wakefulness becomes higher and higher, the bed, music, lighting and other related configurations will change adaptively, such as the music from soft to loud, the light slowly increases, etc. This can give the user a buffer process to provide the user with a better wake-up experience. And through this dynamic mechanism, the energy consumption of the equipment can be reduced to a certain extent, reducing resource waste.

[0039] 3) During the awakening process, the present invention monitors the user's body movement data and determines the user's falling risk under the current target awakening mode based on the user's body movement data; when it is determined that the falling risk is greater than a preset threshold, an alarm is generated and the target awakening mode is optimized. This can reduce the user's falling risk when getting up and improve the safety of using the smart electric bed.

[0040] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the background technology, the drawings required for use in the embodiments of the present invention or the background technology will be described below.

[0042] The drawings herein are incorporated into the specification and constitute a part of the specification. These drawings illustrate embodiments consistent with the present disclosure and are used to illustrate the technical solutions of the present disclosure together with the specification.

[0043] Figure 1 A schematic flow chart of a method for waking up a user of an IoT smart electric bed provided by an embodiment of the present invention;

[0044] Figure 2 The embodiment of the present invention provides Figure 1 Schematic diagram of the process of the sub-steps of step S104;

[0045] Figure 3 A schematic structural diagram of a user wake-up system for an IoT smart electric bed provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0046] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or devices.

[0048] The term "and / or" herein is only a description of the association relationship of the associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the term "at least one" herein represents any combination of at least two of any one or more of a plurality of. For example, including at least one of A, B, and C can represent including any one or more elements selected from the set consisting of A, B, and C.

[0049] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0050] In addition, in order to better illustrate the present invention, numerous specific details are provided in the following specific embodiments. It should be understood by those skilled in the art that the present invention can be implemented without certain specific details. In some examples, methods, means, components and circuits well known to those skilled in the art are not described in detail in order to highlight the subject matter of the present invention.

[0051] During the use of current smart electric beds, the user wake-up function is relatively simple, lacking targeted strategies and dynamic mechanisms, and the method of only setting it by user preferences often lacks scientific guidance, thereby endangering the user's sleep health. For this reason, the present invention provides a user wake-up method for an IoT smart electric bed, which takes into account multi-dimensional factors and individual differences, takes into account scientific wake-up methods and user preferences, and adds a dynamic mechanism, so that the user wake-up methods are highly diverse and can meet the user's needs for a diverse wake-up experience.

[0052] See also Figure 1 , Figure 1 A flowchart of a method for waking up a user of an IoT smart electric bed provided by an embodiment of the present invention. Figure 1 As shown, a user wake-up method for an IoT smart electric bed includes the following steps:

[0053] S10, determining a target awakening method for awakening the user, including:

[0054] S101, monitoring the sleep state of the user to obtain the sleep cycle stage of the user, and recommending a corresponding first wake-up method according to different sleep cycle stages;

[0055] S102, obtaining the identity information and health status information of the user, and recommending a second wake-up method according to the identity information and health status information of the user;

[0056] S103, obtaining user's custom wake-up setting information, and generating a third wake-up method according to the custom wake-up setting information;

[0057] S104, determining a target awakening method according to the first awakening method, the second awakening method, and the third awakening method;

[0058] S20, performing a wake-up operation on the user according to the target wake-up method, monitoring the user's wake-up state during the wake-up process and generating a wake-up state change curve, and adaptively adjusting the target wake-up method according to the wake-up state change curve.

[0059] The target wake-up method usually includes the wake-up time, wake-up action, and wake-up auxiliary environment settings.

[0060] The wake-up time refers to the exact time when the user wants to wake up from sleep. This time is usually set by the user according to his or her work, living habits or health needs. Smart electric beds allow users to set the wake-up time accurately to the minute, and can also recommend the wake-up time according to the user's sleep cycle stage to provide scientific wake-up guidance, such as ensuring that the wake-up is performed at the most suitable light sleep stage to reduce fatigue after getting up.

[0061] Usually, based on the user's preset wake-up time, the system starts to perform the wake-up action when approaching the time, and further combines it with the wake-up of the sleep cycle, using biometric sensors to monitor the user's sleep stage, and starts the wake-up process when approaching the light sleep period to minimize interference and improve wakefulness after waking up. By integrating user preferences and scientific wake-up time of the sleep cycle, the most appropriate wake-up time is determined.

[0062] The awakening action refers to the physical or sensory stimulation method used by the smart mattress to actually awaken the user. The awakening action usually includes body position change awakening, biological vibration awakening and voice control awakening. It can be understood that only one awakening action can be used for awakening, or multiple awakening actions can be used in combination.

[0063] Wake-up by changing body position: For example, the mattress automatically adjusts the angle of the head of the bed or the bed body, allowing the user to wake up in a state closer to a sitting position, which helps reduce the feeling of confusion when just waking up.

[0064] Bio-vibration awakening: For example, air bags or mechanical devices built into the mattress vibrate gently, simulating a gentle massage or hug.

[0065] Voice-activated wake-up: For example, play soft natural sounds or music that the user likes, gradually increase the volume, and wake up the hearing in a gentle way.

[0066] Wake-up assistance environment settings refer to adjustments to the surrounding environment to help users wake up more comfortably and naturally, including but not limited to:

[0067] Light adjustment: Mimics the sunrise effect and gradually increases the light brightness in the bedroom to help users wake up naturally.

[0068] Room temperature regulation: Automatically adjust the room temperature to a temperature that is more conducive to wakefulness through a smart thermostat.

[0069] Blackout curtains open automatically: Let natural light into the room and use light to help the body adjust its biological clock.

[0070] Air Freshener & Humidity Control: Regulates indoor air quality and humidity to create a more comfortable environment to wake up.

[0071] Ambient music or white noise: Play audio that helps you wake up and create a positive morning vibe.

[0072] Before determining the target wake-up method in this embodiment, it is first necessary to determine three wake-up methods based on three different dimensions. Each wake-up method usually requires providing wake-up time, wake-up action, and wake-up auxiliary environment settings. Finally, the final target wake-up method is determined by comprehensively considering the wake-up methods in the three dimensions.

[0073] Step S101 is intended to recommend the first wake-up method based on the sleep cycle. Various sensors are usually configured in smart electric beds, including pressure sensors, heart rate sensors, body temperature sensors, accelerometers, etc., which are used to monitor the user's physiological indicators and body movements in real time. In some special scenarios, non-contact radar wave detection and sound monitoring (such as snoring) may also be used in advanced models to capture sleep characteristics more comprehensively. Based on sensor data, multi-dimensional data collection can be performed first, and the sleep cycle recognition algorithm can be used to identify the user's sleep cycle stage, including rapid eye movement sleep (REM), light sleep, deep sleep and other different stages.

[0074] In one embodiment, recommending corresponding first wake-up methods according to different sleep cycle stages includes:

[0075] Wake up in light sleep: Users are easier to wake up in this stage and feel more refreshed after waking up. The wake-up program will be started at the best time when the user enters light sleep.

[0076] Avoid waking up users during deep sleep: Avoid waking up users during deep sleep, as waking up at this time may cause users to feel drowsy and uncomfortable, affecting their work and study efficiency after waking up.

[0077] The above method determines the wake-up time according to the sleep cycle, and for the wake-up action and the wake-up auxiliary environment setting, the contents of the above embodiment can be referred to. Therefore, by distinguishing the sleep cycle stages, the wake-up time can be provided scientifically, and then the first wake-up method can be recommended in combination with the wake-up action and the wake-up auxiliary environment setting.

[0078] In one embodiment, the user's identity information includes the user's age and occupation information; the health status information includes the user's alcohol influence status, physiological cycle status, and physical disease status. Step S102 is intended to recommend a second wake-up method based on the user's identity information and health status information, specifically:

[0079] User age: Younger people may be more adaptable to earlier or more abrupt awakenings, while older people may require a more gentle, gradual awakening process as their sleep cycles and body adaptability may be different.

[0080] Occupational information: For occupations that require high concentration, such as pilots, doctors, lawyers, etc., it is recommended to wake up in the light sleep stage shortly after the end of deep sleep to ensure the best cognitive state throughout the day. For creative workers, more flexible wake-up times may be allowed, allowing them to have more time for creative thinking after waking up naturally.

[0081] Health status information:

[0082] Alcohol-affected state: If the user has drunk alcohol the night before, considering that alcohol may prolong deep sleep and affect sleep quality, the intelligent system should postpone the wake-up time until the alcohol is metabolized, and use gentler methods, such as gradually brightening lights and natural sounds, to reduce discomfort.

[0083] Menstrual cycle status: Women’s sleep quality and needs may change during different stages of their menstrual cycle. For example, longer sleep duration and gentler wake-ups may be recommended around menstruation to alleviate possible fatigue and mood swings.

[0084] Physical illness: Users with certain diseases, such as heart disease and sleep apnea, may need to pay special attention to the way they are awakened. For example, for users with heart problems, avoid using sudden strong vibrations to wake them up, and choose gentle sounds or light changes. For patients with sleep apnea, ensure that body position adjustments during awakening do not aggravate symptoms.

[0085] Step S103 mainly considers user preferences. Although the wake-up methods are scientifically recommended based on sleep cycles, user identities and health status in steps S101 and S102 respectively, if the user's individual preferences are ignored, the user's better waking experience cannot be satisfied. Therefore, step S103 mainly generates a third wake-up method based on the custom wake-up setting information.

[0086] After the first wake-up method, the second wake-up method and the third wake-up method are determined, in order to facilitate the determination of the target wake-up method, the wake-up time, wake-up action and wake-up auxiliary environment settings under each wake-up method can be displayed in a list, so that in S104, the target wake-up method can be quickly determined based on the first wake-up method, the second wake-up method and the third wake-up method.

[0087] In summary, the target awakening method provided by the present invention takes into account the awakening method under multi-dimensional factors. The first awakening method is mainly recommended scientifically based on scientific sleep cycles. The second awakening method mainly considers the impact of the user's identity information and health status information on the user's awakening, where the user's identity information includes the user's age and occupation information; the health status information includes the user's alcohol influence status, physiological cycle status, and physical disease status. The third awakening method is mainly based on the user's own preferences. By combining these three methods, the best target awakening method for the user's current awakening scenario is determined, which can not only meet the user's preferences, but also provide a scientific and healthy awakening method, which is more conducive to guiding the user's healthy work and rest.

[0088] In one embodiment, when the target wake-up method is obtained, a wake-up operation is performed on the user according to the target wake-up method in step S20, the user's wake-up state is monitored during the wake-up process and a wake-up state change curve is generated, and the target wake-up method is adaptively adjusted according to the wake-up state change curve.

[0089] In this embodiment, the effectiveness of the current wake-up method is dynamically evaluated based on the wake-up state change curve. If it is found that the user wakes up slower than expected or there are signs of repeatedly entering shallow sleep, the wake-up strategy is automatically adjusted. For example:

[0090] Increase stimulation: If the user is slow to respond, gradually increase the volume of the music or the brightness of the light.

[0091] Gentle wake-up: If it is detected that the user has a stress response to the current wake-up method (such as a fast heart rate), the stimulation intensity will be reduced and replaced with a gentler method, such as using only natural sounds.

[0092] Therefore, compared with the existing mechanized wake-up program, this embodiment can dynamically adjust the wake-up method. For example, as the user becomes more awake, the bed, music, lighting and other related configurations will change adaptively, such as music from soft to loud, light slowly increasing, etc. This can give the user a buffer process to provide the user with a better wake-up experience. And through this dynamic mechanism, it can also reduce equipment energy consumption and resource waste to a certain extent.

[0093] See also Figure 2In one embodiment, determining the target wake-up mode according to the first wake-up mode, the second wake-up mode and the third wake-up mode comprises the following sub-steps:

[0094] S1041: construct an evaluation factor set according to the first awakening method, the second awakening method, and the third awakening method, and determine a corresponding level set:

[0095] Evaluation factor set U = {F1, F2, F3};

[0096] Level set V = {V1, V2, V3, V4, V5};

[0097] Wherein, F1, F2, and F3 represent the first wake-up mode, the second wake-up mode, and the third wake-up mode, respectively;

[0098] V1, V2, V3, V4, and V5 represent five levels of suitability, such as very unsuitable, not very suitable, moderately suitable, relatively suitable, and very suitable.

[0099] S1042. Based on each factor in the evaluation factor set, a corresponding fuzzy evaluation matrix is ​​constructed. The items in the fuzzy evaluation matrix are used to represent the membership of different evaluation factors at different levels.

[0100] For each factor F i , construct a fuzzy evaluation matrix R i , where r ij It represents the membership of the i-th factor at the j-th level. These values ​​can usually be obtained through expert scoring, historical data analysis or user surveys, and are normalized to ensure that the sum of the membership of each factor is 1.

[0101] S1043. Determine the weight of each factor using the analytic hierarchy process, and determine the fuzzy evaluation vector based on the weight of each factor and the fuzzy evaluation matrix.

[0102] The weight of each factor is determined by AHP (analytic hierarchy process). i , ensure that all a i The sum is 1

[0103] Determine the fuzzy evaluation vector:

[0104]

[0105] Where n is the number of factors, a i is the weight of the i-th factor, R i is the fuzzy evaluation matrix of the i-th factor.

[0106]

[0107] Where m is the number of elements in the level set V, r ij represents the membership of the i-th factor at the j-th level, v kj Is level v j The vector representation of .

[0108] S1044. Compare the fuzzy evaluation vector with the grade set, screen out the target grade corresponding to the maximum membership degree and the target factor corresponding to the target grade, and use the awakening method under the target factor as the target awakening method.

[0109] The comprehensive fuzzy evaluation vector B is compared with the level set V to find the level corresponding to the maximum membership, which is an indication of the suitability of the final awakening method. Specifically, the membership function of each level can be calculated, and the level with the largest membership function is selected as the recommended awakening method.

[0110] Therefore, this embodiment uses the fuzzy comprehensive evaluation method to comprehensively consider all influencing factors of the first wake-up method, the second wake-up method, and the third wake-up method, as well as their relative importance, so as to more accurately determine the final wake-up strategy, thereby avoiding uncertainty and subjective judgment and making the wake-up decision closer to complex user behavior patterns.

[0111] During the execution of the wake-up operation, if the head of the bed is not raised at an appropriate angle or the user's posture is risky, there may be a risk of falling under the bed, posing a safety hazard. To this end, in one embodiment, when the wake-up method performs the wake-up operation on the user according to the target wake-up method, it also includes:

[0112] Monitor the user's body motion data during the awakening process, and determine the user's falling risk under the current target awakening method based on the user's body motion data;

[0113] When it is determined that the falling risk is greater than the preset threshold, an alarm prompt is generated and the target wake-up method is optimized.

[0114] In this embodiment, when optimizing the target wake-up method, it is necessary to first analyze the current wake-up method: determine the currently used wake-up method (such as sound, light, vibration, etc.) and its effect evaluation indicators (such as response time, false wake-up rate). Further evaluate the risk scenario: Considering the risk of falling, the optimization strategy should focus on waking up the target quickly and accurately. For example, in a high-risk fall scenario, a more direct and strong wake-up method may be required, such as emergency vibration + high-pitched alarm. Finally, you can choose to use a new wake-up method. For example, if it was originally a sound wake-up, it can be changed to a high-intensity sound combined with vibration double wake-up, etc.

[0115] In one embodiment, determining the falling risk of the user in the current target awakening mode according to the user's body motion data includes:

[0116] Detect the weight distribution of the user on the electric bed surface and obtain the average weight distribution of the user outside the safety range of the bed surface;

[0117] Detect the user's heart rate variability and user activity, and calculate the user's wakefulness coefficient based on the heart rate variability;

[0118] Get the head lift angle of the electric bed, and calculate the fall risk index based on the head lift angle, average weight distribution, user activity, and wakefulness coefficient:

[0119] FRI=w1G×cosθ+w2(1-M)+w3C s +w4(E e +E h );

[0120] Where FRI represents the fall risk index, G represents the average weight distribution of the user outside the safe range of the bed surface, M represents the user's activity, θ represents the angle of the head of the bed, and C s Indicates the user's awake state coefficient, E e 、E h They represent environmental compensation factor and age compensation factor respectively, and w1, w2, w3, and w4 represent weights respectively.

[0121] In this embodiment, the safety range of the bed surface can be set according to the distance from the edge of the bed. For example, if the bed surface is a rectangular structure of 1.5*2 meters, the safety range can be set to within 0.2m from the left and right edges. In this way, the average weight distribution of users outside the safety range of the bed surface can be determined. The smaller this value is, the closer the user is to the bed, and the higher the risk.

[0122] The angle of the head of the bed is good for waking up the user, but as the angle increases, the potential risk will also increase, leading to the risk of falling. This data is usually collected by the angle sensor. User activity can usually be evaluated through motion sensor data. The larger the value, the stronger the activity ability and the lower the risk. The awake state coefficient is mainly calculated based on heart rate variability. A value close to 1 indicates full awakeness, and a low value indicates possible drowsiness or sleeping, which is a higher risk. After determining these parameters, you only need to substitute them into the above formula to get the size of the fall risk index FRI. If the value is greater than the preset threshold, an alarm will be triggered immediately, and the target wake-up method will be optimized at the same time, such as adjusting the angle of the head of the bed and lowering the volume of the music.

[0123] During the awakening process, this embodiment monitors the user's body movement data and determines the user's falling risk under the current target awakening method based on the user's body movement data; when it is determined that the falling risk is greater than the preset threshold, an alarm is generated and the target awakening method is optimized. This can reduce the user's falling risk when getting up and improve the safety of using the smart electric bed.

[0124] In one embodiment, the smart electric bed used in the above awakening method usually needs to select a suitable drive system, such as a precise and quiet electric push rod, pneumatic cylinder or linear motor and other drive devices to achieve smooth lifting and lowering of the head, foot or entire bed. Sensors can usually use integrated sensors. In order to achieve automatic awakening, the bed may need to integrate biometric sensors, such as heart rate sensors, body motion sensors, to determine the user's sleep state, pressure sensors to determine the user's weight distribution, angle sensors to collect bed angle signals, etc. This ensures that the awakening process starts at the right time and improves safety during the awakening process.

[0125] Furthermore, in order to achieve linkage with smart home systems, electric beds should have standard network interfaces such as Wi-Fi, Bluetooth, Zigbee, etc. and support relevant communication protocols (such as MQTT, Z-Wave, etc.) to exchange data with smart lights, speakers and other devices. As for user interface development, easy-to-use mobile phone applications or bedside control panels can be designed to allow users to easily set wake-up time, choose wake-up methods, view sleep data, and link with other smart home devices.

[0126] In one embodiment, since both sleep data and wake-up data involve the user's personal privacy, the data collected by the sensor must be encrypted to ensure data transmission and storage security. For example, a symmetric encryption algorithm AES can be used. This algorithm can play a good encryption protection role for a large amount of user data to prevent data leakage. Preferably, data security can also be improved through user authority management, such as setting up a reasonable user authority system to ensure the user's access and control over personal data and comply with relevant data protection regulations.

[0127] See also Figure 3 In one embodiment, the present invention further provides a user wake-up system for an IoT smart electric bed, the system comprising:

[0128] The wake-up mode determination module 100 is used to determine a target wake-up mode for waking up the user, including:

[0129] The first recommendation unit 101 is used to monitor the user's sleep state to obtain the sleep cycle stage of the user, and recommend a corresponding first wake-up method according to different sleep cycle stages;

[0130] The second recommendation unit 102 is used to obtain the identity information and health status information of the user, and recommend a second wake-up method according to the identity information and health status information of the user;

[0131] The third recommendation unit 103 is used to obtain the user's customized wake-up setting information and generate a third wake-up method according to the customized wake-up setting information;

[0132] A target mode generating unit 104, configured to determine a target wake-up mode according to the first wake-up mode, the second wake-up mode and the third wake-up mode;

[0133] The adaptive adjustment module 20 is used to perform a wake-up operation on the user according to the target wake-up method, monitor the user's wake-up state during the wake-up process and generate a wake-up state change curve, and adaptively adjust the target wake-up method according to the wake-up state change curve.

[0134] It can be understood that the functions or modules included in the system provided in this embodiment can be used to execute the method described in the above method embodiment. Its specific implementation can refer to the description of the above method embodiment. For the sake of brevity, it will not be repeated here.

[0135] The present invention also provides an electronic device, comprising: a processor and a memory, wherein the memory is used to store computer program code, the computer program code includes computer instructions, and when the processor executes the computer instructions, the electronic device executes a method as described in any possible implementation manner.

[0136] The present invention also provides a computer-readable storage medium, in which a computer program is stored. The computer program includes program instructions. When the program instructions are executed by a processor of an electronic device, the processor executes a method as described in any possible implementation manner.

[0137] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0138] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. Those skilled in the art can also clearly understand that the descriptions of the various embodiments of the present invention have different focuses. For the convenience and brevity of description, the same or similar parts may not be repeated in different embodiments. Therefore, for parts not described or not described in detail in a certain embodiment, refer to the records of other embodiments.

[0139] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0140] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented by software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions can be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a digital versatile disc (DVD)), or a semiconductor medium (eg, a solid state disk (SSD)).

[0141] A person skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by a computer program to instruct the relevant hardware, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above-mentioned method embodiments. The aforementioned storage medium includes: a read-only memory (ROM) or a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

Claims

1. A user wake-up method for an IoT smart electric bed, characterized in that: The method comprises: Determine the target wake-up method used to wake the user, including: Monitor the user's sleep state to obtain the user's sleep cycle stage, and recommend a corresponding first wake-up method according to different sleep cycle stages; Obtaining the user's identity information and health status information, and recommending a second wake-up method based on the user's identity information and health status information; Obtaining user's customized wake-up setting information, and generating a third wake-up method according to the customized wake-up setting information; Determine a target awakening method according to the first awakening method, the second awakening method and the third awakening method; Performing a wake-up operation on the user according to the target wake-up method, monitoring the user's wake-up state during the wake-up process and generating a wake-up state change curve, and adaptively adjusting the target wake-up method according to the wake-up state change curve; The performing a wake-up operation on the user according to the target wake-up mode also includes: Monitor the user's body motion data during the awakening process, and determine the user's falling risk under the current target awakening method based on the user's body motion data; When it is determined that the falling risk is greater than the preset threshold, an alarm is generated and the target wake-up method is optimized; The determining the falling risk of the user in the current target awakening mode according to the user's body motion data includes: Detect the weight distribution of the user on the electric bed surface and obtain the average weight distribution of the user outside the safety range of the bed surface; Detect the user's heart rate variability and user activity, and calculate the user's wakefulness coefficient based on the heart rate variability; Get the head lift angle of the electric bed, and calculate the fall risk index based on the head lift angle, average weight distribution, user activity, and wakefulness coefficient: FRI=w1G×cosθ+w2(1-M)+w3C s +w4(E e +E h ); Where FRI represents the fall risk index, G represents the average weight distribution of the user outside the safe range of the bed surface, M represents the user's activity, θ represents the angle of the head of the bed, and C s Indicates the user's awake state coefficient, E e 、E h They represent environmental compensation factor and age compensation factor respectively, and w1, w2, w3, and w4 represent weights respectively.

2. The user awakening method for the IoT smart electric bed according to claim 1, characterized in that: The determining the target wake-up mode according to the first wake-up mode, the second wake-up mode and the third wake-up mode includes: constructing an evaluation factor set according to the first awakening method, the second awakening method, and the third awakening method, and determining a corresponding level set; Based on each factor in the evaluation factor set, a corresponding fuzzy evaluation matrix is ​​constructed, and the items in the fuzzy evaluation matrix are used to represent the membership of different evaluation factors at different levels; The weight of each factor is determined by using the analytic hierarchy process, and the fuzzy evaluation vector is determined according to the weight of each factor and the fuzzy evaluation matrix; The fuzzy evaluation vector is compared with the grade set, the target grade corresponding to the maximum membership degree and the target factor corresponding to the target grade are screened out, and the awakening method under the target factor is used as the target awakening method.

3. The user awakening method for the IoT smart electric bed according to claim 1, characterized in that: The user's identity information includes the user's age and occupation information; the health status information includes the user's alcohol influence status, physiological cycle status and physical disease status.

4. The user awakening method for the IoT smart electric bed according to claim 1, characterized in that: The target awakening method includes awakening time, awakening action and awakening auxiliary environment setting; the awakening action includes any one or more of body position change awakening, biological vibration awakening and voice control awakening.

5. A user wake-up system for an IoT smart electric bed, characterized in that: The system comprises: The wake-up mode determination module is used to determine the target wake-up mode for waking up the user, including: A first recommendation unit is used to monitor the user's sleep state to obtain the user's sleep cycle stage, and recommend a corresponding first wake-up method according to different sleep cycle stages; A second recommendation unit, configured to obtain the identity information and health status information of the user, and recommend a second wake-up method according to the identity information and health status information of the user; A third recommendation unit, configured to obtain user-defined wake-up setting information and generate a third wake-up method according to the customized wake-up setting information; A target mode generating unit, configured to determine a target wake-up mode according to the first wake-up mode, the second wake-up mode and the third wake-up mode; An adaptive adjustment module is used to perform a wake-up operation on the user according to the target wake-up mode, monitor the user's wake-up state during the wake-up process and generate a wake-up state change curve, and adaptively adjust the target wake-up mode according to the wake-up state change curve; The performing a wake-up operation on the user according to the target wake-up mode also includes: Monitor the user's body motion data during the awakening process, and determine the user's falling risk under the current target awakening method based on the user's body motion data; When it is determined that the falling risk is greater than the preset threshold, an alarm is generated and the target wake-up method is optimized; The determining the falling risk of the user in the current target awakening mode according to the user's body motion data includes: Detect the weight distribution of the user on the electric bed surface and obtain the average weight distribution of the user outside the safety range of the bed surface; Detect the user's heart rate variability and user activity, and calculate the user's wakefulness coefficient based on the heart rate variability; Get the head lift angle of the electric bed, and calculate the fall risk index based on the head lift angle, average weight distribution, user activity, and wakefulness coefficient: FRI=w1G×cosθ+w2(1-M)+w3C s +w4(E e +E h ); Where FRI represents the fall risk index, G represents the average weight distribution of the user outside the safe range of the bed surface, M represents the user's activity, θ represents the angle of the head of the bed, and C s Indicates the user's awake state coefficient, E e 、E h They represent environmental compensation factor and age compensation factor respectively, and w1, w2, w3, and w4 represent weights respectively.

6. An electronic device, characterized in that: include: A processor and a memory, wherein the memory is used to store computer program codes, wherein the computer program codes include computer instructions, and when the processor executes the computer instructions, the electronic device executes the user wake-up method for an IoT smart electric bed as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which includes program instructions. When the program instructions are executed by a processor of an electronic device, the processor executes the user wake-up method for an IoT smart electric bed as described in any one of claims 1 to 4.

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